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Performance marketers in all categories are in a constant tug-of-war trying to scale acquisition while keeping costs low. Many fall into the trap of relying almost exclusively on optimization and miss out on efficiencies to be gained with a more thoughtful and strategic approach to targeting.

Facebook offers a powerful mix of targeting parameters that can’t be beat when employed in the right combination. Each targeting technique has its own strengths and weaknesses, but used together the downside of each is greatly mitigated:

Strengths

Weaknesses

Custom Audiences

·Audience is already qualified

·Scale is limited to your known audience

Lookalike Audiences

Ability to scale quickly & easily

Audience is highly qualified

Exceptional ROAS

·Audience dilutes with scale beyond a certain level

Interest Audiences

· Maximum scale

· High upside on performance when used correctly

·Great for launching new apps/brands or in new markets

·Selecting the right interests can be challenging

Determining which mix of targeting tactics to use depends on your marketing objectives and your lifecycle stage. The two scenarios we see the most often and that cause the most pain for app and brand marketers include:

How to launch a new app or in a new market more efficiently

Facebook lookalike audiences have been a god-send for marketers. They scale quickly and the performance is impossible to beat up to a certain level. However, if you don’t have a quality seed audience you aren't setting up the lookalike model for success..

The lack of a seed audience is often a challenge for new apps and for established apps that want to extend into a new market. This reality leads many marketers to take a “spray and pay” approach where they spend money and try to optimize while a campaign is in-market. As you might imagine, that leads to inefficient spend.

Marketers that approach this new market/new app scenario by leaning on interest audiences have the right idea, but generally execution falls flat. They may target competitors or large generic interests like “strategy games” or “fitness” depending on the app category and are disappointed when acquisition costs remain high.

A more nuanced and strategic approach is to rely on data to determine which interests to target. There are two main ways to accomplish this goal:

Expand on what works

If you have interests that perform well, Facebook will suggest new similar interests that will help add incremental scale.

The problem is that if you want to know which interests are working, you have to run every ad set with just one interest. The other option is to use Appnique’s Facebook Diagnostics tool for full transparency into how each interest performs and add similar interests in a couple clicks.

Build a new interest audience that works

The operative word here is “works”. Without a data-informed way of selecting interests you are limited to guesses. That creates two challenges:

Competitors in your category probably made the same guess and are bidding up those interests

There are hundreds of interests you’re not considering

Using another pattern of logic based on affinity can help you select relevant interests that your competitors are less likely to be bidding on. However, a more effective route is to base your decisions in data.

Similar to how Facebook builds high quality lookalike audiences by analyzing data about everyone on the network, Appnique builds lookalike-style audiences based on user data and behavior from app stores and maps it back to Facebook for easy targeting. Essentially, you can build a lookalike audience without a seed.

How to add scale for mature app campaigns

Many app marketers rely almost exclusively on lookalike audiences to achieve growth. For some, that may be all they need. However, for those who want to rapidly scale beyond a certain level, lookalike audiences may become costly. The challenge is that because of how they are designed at a certain point the audience can become diluted.

For mature apps, it likely that the category is highly competitive so selecting the right interest audience is even more challenging. It’s important to have a diverse set of interest audiences based on different hypotheses. In doing so, you mitigate the risk that your best performers in any ad set might be bid up and become more expensive. You also cast a wider net and create more opportunity to expand on what works in any given ad set.

There are a few different approaches you can take using Appnique’s suite of tools:

Build a lookalike-style audience from your own user base from app store data